[1]}
propose a certain relaxed convex potential function, which we denote \(\phi ^\star \) , and prove that it is “RCN-robust”.[1]} uses the term “SLN-robust”, where the acronym stands for Symmetric Label Noise. We give a formal definition of RCN-robustness in sec:preliminaries, but intuitively it means that a minimizer of this potential function (minimizing over all linear combinations of base features) under random classification noise performs no worse than a minimizer obtained with no random classification noise. [1]} also define a notion of “strong RCN-robustness”, show that their \(\phi ^\star \)  is the unique relaxed convex potential function which satisfies strong RCN-robustness.
